johnbaums

diverge0 <- function(p, ramp) {
  # p: a trellis object resulting from rasterVis::levelplot
  # ramp: the name of an RColorBrewer palette (as character), a character 
  #       vector of colour names to interpolate, or a colorRampPalette.
  require(RColorBrewer)
  require(rasterVis)
  if(length(ramp)==1 && is.character(ramp) && ramp %in% 
     row.names(brewer.pal.info)) {
    ramp <- suppressWarnings(colorRampPalette(brewer.pal(11, ramp)))
  } else if(length(ramp) > 1 && is.character(ramp) && all(ramp %in% colors())) {

johnbaums

#### Temporal Interpolation ####################################################
# Perform cell-wise linear interpolation between multiple raster layers, and 
# extrapolation beyond the upper limit of input data. Output is saved in .tif 
# format.
#
# Arguments
# s: a rasterStack containing the time slices to be interpolated 
#
# xin: a numeric vector that indicates the times associated with layers in s (in
# the same order as the layers of s - see names(s))

johnbaums

.bubbles {
  stroke-width: 1px;
  stroke: black;
  opacity: .8
}
.bubbles:hover {
  stroke: black;
}

* {

johnbaums

exdet <- function (ref, p, mic=TRUE, tol, quiet=FALSE) {
# ref: data.frame of environments at reference locations.
  # p: data.frame of environments to assess.
  # mic: should the most influential covariates also be returned?
  # quiet: should messages be suppressed?
  # tol: tolerance, passed to mahalanobis(). See ?solve.
  # =============================================================
  # When passing `mic=TRUE`, a data.frame will be returned with 3 
  #  columns giving the exdet score (D), the most influential
  #  covariate with respect to type 1 novelty (MIC1; equivalent to 

johnbaums

sf_arrow <- function(from, to, len, deg, prefixFrom='from.', prefixTo='to.') {
  # from, to: sf objects. `from` should be either a single point, or the same
  #   number of points as in `to`.
  # len: the length of the arrow head, in map units.
  # deg: the angle (in degrees) of the arrow head.
  # prefixFrom, prefixTo: the attributes of `from` and `to` are added to the 
  #  returned sf object. These prefixes will be prepended to the existing names
  #  of `from` and `to`.
  crs <- st_crs(to)
  n <- nrow(to)

johnbaums

library(dplyr)
library(jsonlite)

# create an empty list to store results
items <- list()
i <- 1 # initialise counter to 1. This is used to specify the start index for queries

# We can download at most 100 records at a time, so we download in pages,
# increasing the start index by 100 each time. We do this until the result of 
# the query has fewer than 100 results (i.e. we've retrieved the last page of

johnbaums

dms2dd <- function(x) {
  dms <- strsplit(x, '[^0-9.]+')
  sapply(dms, function(x) {
    sum(as.numeric(x)/c(1, 60, 3600)[seq_along(x)])
  })
}

dms2dd(c('34°53’07”S', '138°36’40”E', '138.61111', '34°53’'))
## [1]  34.88528 138.61111 138.61111  34.88333

johnbaums

get_namespaces <- function(file, as_imports=FALSE) {
  require(stringi)
  require(dplyr)
  
  nm <- readLines(file) %>% 
    stringi::stri_extract_all(regex='[\\w_.]+::[\\w_.]+') %>% 
    unlist %>% 
    setdiff(NA) %>% 
    sort
  

johnbaums

export PYTHON_VERSION=3.4.3
export PYTHON_SHORT_VERSION=3.4
export GEOS_VERSION=3.6.2
export GDAL_VERSION=2.2.2
export PROJ4_VERSION=4.9.3

sudo yum-config-manager --enable epel
sudo yum install gdal-python
sudo yum -y install make automake gcc gcc-c++ libcurl-devel proj-devel geos-devel

johnbaums

gdal_mean <- function(infile, outfile, return_raster=FALSE, overwrite=FALSE) {
  # Be aware that the outfile type will be the same as the infile type
  require(rgdal)
  if(return_raster) require(raster)
  # infile:        The multiband raster file (or a vector of paths to multiple 
  #                 raster files) for which to calculate cell mean.
  # outfile:       Path to raster output file.
  # return_raster: (logical) Should the output raster be read back into R?
  # overwrite:     (logical) Should outfile be overwritten if it exists?
  gdal_calc <- Sys.which('gdal_calc.py')